Discriminative Locality Alignment
نویسندگان
چکیده
Fisher’s linear discriminant analysis (LDA), one of the most popular dimensionality reduction algorithms for classification, has three particular problems: it fails to find the nonlinear structure hidden in the high dimensional data; it assumes all samples contribute equivalently to reduce dimension for classification; and it suffers from the matrix singularity problem. In this paper, we propose a new algorithm, termed Discriminative Locality Alignment (DLA), to deal with these problems. The algorithm operates in the following three stages: first, in part optimization, discriminative information is imposed over patches, each of which is associated with one sample and its neighbors; then, in sample weighting, each part optimization is weighted by the margin degree, a measure of the importance of a given sample; and finally, in whole alignment, the alignment trick is used to align all weighted part optimizations to the whole optimization. Furthermore, DLA is extended to the semi-supervised case, i.e., semi-supervised DLA (SDLA), which utilizes unlabeled samples to improve the classification performance. Thorough empirical studies on the face recognition demonstrate the effectiveness of both DLA and SDLA.
منابع مشابه
DLANet: A manifold-learning-based discriminative feature learning network for scene classification
This paper presents Discriminative Locality Alignment Network (DLANet), a novel manifold-learningbased discriminative learnable feature, for wild scene classification. Based on a convolutional structure, DLANet learns the filters of multiple layers by applying DLA and exploits the block-wise histograms of the binary codes of feature maps to generate the local descriptors. A DLA layer maximizes ...
متن کاملSecurity Classification of : 1 . Report Date ( Dd - Mm - Yyyy )
Locality-Constrained Discriminative Learning and Coding Report Title This paper explores the enhancement by locality constraint to both learning and coding schemes, more specifically, discriminative low-rank dictionary learning and auto-encoder. Previous Fisher discriminative based dictionary learning has led to interesting results by learning more discerning sub-dictionaries. Also, the low-ran...
متن کاملPainless Unsupervised Learning with Features
We show how features can easily be added to standard generative models for unsupervised learning, without requiring complex new training methods. In particular, each component multinomial of a generative model can be turned into a miniature logistic regression model if feature locality permits. The intuitive EM algorithm still applies, but with a gradient-based M-step familiar from discriminati...
متن کاملSigned Laplacian Embedding for Supervised Dimension Reduction
Manifold learning is a powerful tool for solving nonlinear dimension reduction problems. By assuming that the high-dimensional data usually lie on a low-dimensional manifold, many algorithms have been proposed. However, most algorithms simply adopt the traditional graph Laplacian to encode the data locality, so the discriminative ability is limited and the embedding results are not always suita...
متن کاملStructural Local Multiple Alignment of RNA
Today, RNA is well known to perform important regulatory and catalytic function due to its distinguished structure. Consequently, state-of-the-art RNA multiple alignment algorithms consider structure as well as sequence information. However, existing tools neglect the important aspect of locality. Notably, locality in RNA occurs as similarity of subsequences as well as similarity of only substr...
متن کامل